In AI, a “small error” is never small. A model that’s just 2% less accurate for one demographic might seem insignificant in the lab. But once deployed to millions of users, that gap can turn into systemic exclusion — and at scale, it becomes a global inequity machine.

I’ve seen this across industries. In healthcare, slightly underestimating risk scores for certain populations meant thousands missed out on preventative care. In finance, a fractional difference in loan approval rates translated to billions in lost opportunity for entire communities. The scary part? These systems all passed internal reviews before launch. The bias was subtle enough to hide until scale amplified it.

In this talk, I’ll break down exactly how bias scales in the wild, and how organizations can build early-warning systems to detect it before it snowballs. We’ll explore:

“Scaling risk” metrics — spotting bias that looks harmless in testing but dangerous in deployment

Bias drift detection across geographies, demographics, and time

Governance frameworks for global AI deployments that adapt to local equity needs

Why it matters now: In 2025, AI is scaling faster than regulation can adapt. The organizations that learn to measure and manage bias at scale will not only protect their users but will also avoid massive brand, legal, and social fallout.

Attendees will leave with a clear, actionable framework for stopping small errors from becoming large-scale harm.

Soundbite: “In AI, bias doesn’t just scale — it snowballs.”